Implementation of Deep Learning Approach for Detection and Prediction of Breast Cancer

Project Domain / Category

Artificial Intelligence: Deep learning

Abstract/Introduction

Early detection and prediction of medical problems can save the life of human beings. It is
complicated and expensive to detect on manual basis detection regularly by domain experts
and it will not give accurate predictions. Artificial intelligence techniques are the better
approach to automatic cancer disease detection and diagnosis with highly accurate results.
Breast cancer is an important factor affecting women’s health. The program will be
implemented to detect and predict breast cancer diseases by using deep learning methods
such as the classification of normal, benign, and malignant tissues. In this system, it will be
considered requirements that utilize breast cancer images repository datasets for
experimentation.

Functional Requirements:

1. There are seven major tasks you will typically perform when developing a system.
   Tasks (2-7) should be implemented while developing the system.
   i. Task 1: Select the image datasets of breast cancer disease.
   ii. Task 2: Image Data Analysis and Pte-processing
   iii. Task 3: Feature Extraction
   iv. Task 4: Detection
   v. Task 5: Build system
   vi. Task 6: Test System
   vii. Task 7: Tune System
2. The program should have a knowledge-based system according to select image data.
3. The program should have the deep learning approach to execute model for detection.
4. The program should evaluate the performance and update knowledge based on the
   requirement.

Note:

Skype sessions must be attended to communicate with the supervisor about deep
learning methods and dataset’s discussion otherwise project will not be accepted.

Tools/language:

Python programming language,
Advanced libraries: Keras, OpenCV, NumPy, Pillow, SciPy, and TensorFlow etc